Prototypicality effects in global semantic description of objects
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We propose a new approach to face the semantic features descriptions of objects based in the prototypicality effects of prototypes theory. Our descriptor, called global semantic descriptor, is capable of coding and storing a semantic (central and peripheral) meaning of object. Our model compute the semantic prototype of object using features extracted by CNN-classifications models. We propose a simple method to reduce the dimensionality of semantic prototype without semantic information lost. We demonstrated that our descriptor preserved de semantic information used by the CNN-models for classifications task. The experiments on MNIST and ImageNet Datasets demonstrated that our model allows clustering the elements into family resemblance (typicality value) within the category.
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